Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented volume and speech frequency level adjustment method, the method comprising: learning a preferred level and a characteristic of at least one of audio volume and speech frequency from a historical call or conference conversation; detecting a contextual characteristic of an ongoing conversation and an interaction of a user with a device; determining a cognitive state and a contextual situation of the user in relation to the ongoing conversation as a function of at least one of the contextual characteristic, a preferred level and a characteristic of the volume or the speech frequency, and a user interaction pattern with one or more call or conversation devices; determining at least one factor to trigger an audio level modulation based on the function; and dynamically adjusting audio levels of the ongoing conversation for the user based on the at least one factor.
This invention relates to a computer-implemented method for dynamically adjusting audio volume and speech frequency levels during calls or conference conversations. The method addresses the problem of static audio settings that do not adapt to changing user needs, such as varying cognitive states or contextual situations, leading to suboptimal listening experiences. The method first learns a user's preferred audio volume and speech frequency characteristics from historical call or conference data. During an ongoing conversation, it detects contextual characteristics (e.g., background noise, conversation topic) and user interactions with the device (e.g., adjusting volume manually, muting). Using this data, the method determines the user's cognitive state (e.g., distracted, focused) and contextual situation (e.g., noisy environment, quiet setting). It then evaluates these factors to decide whether to modulate the audio levels dynamically. The system adjusts the audio output in real-time, ensuring optimal volume and speech clarity based on the user's preferences, current context, and interaction patterns. This adaptive approach enhances user experience by automatically compensating for environmental changes or cognitive load, reducing the need for manual adjustments. The method applies to various devices used for calls or conversations, ensuring seamless integration into existing communication systems.
2. The method of claim 1 , further comprising receiving, analyzing and storing a plurality of volume preferred levels and characteristics of the audio adjustment data sources including: a historical volume of audio level of a given user; the historical call or conference conversation; a user feedback; a user profile; and data from one or more sensors of a call or conversation device including historical interactions of a user with the conversation device.
This invention relates to audio adjustment systems for call or conference devices, addressing the challenge of dynamically optimizing audio levels based on user preferences and contextual data. The method involves collecting and analyzing multiple data sources to determine preferred volume levels and characteristics for audio adjustments. These sources include historical audio level data specific to a given user, past call or conference conversations, user feedback, user profiles, and sensor data from the call or conversation device. The sensor data may include historical interactions between the user and the device, such as adjustments made during previous calls or conferences. By analyzing this aggregated data, the system can predict and apply optimal audio settings tailored to the user's preferences and situational context, enhancing the overall audio experience during calls or conferences. The method ensures that volume levels and audio characteristics are dynamically adjusted based on real-time and historical data, improving user satisfaction and communication clarity.
3. The method of claim 1 , further comprising creating and progressively maintaining a database of the preferred level and the characteristic of the volume or the speech frequency with cognitive feedback for a plurality of users.
This invention relates to speech processing systems that adapt to user preferences for volume and speech frequency characteristics. The problem addressed is the lack of personalized audio settings in conventional systems, which often require manual adjustments or fail to account for individual hearing preferences or cognitive feedback. The invention provides a method for dynamically adjusting audio output based on user-specific preferences, including volume levels and speech frequency characteristics, to enhance listening comfort and comprehension. The method involves monitoring user interactions with audio content to determine preferred settings. These preferences are stored in a database that is progressively updated with cognitive feedback from multiple users. Cognitive feedback may include explicit user input or implicit data derived from listening behavior, such as adjustments made to volume or frequency settings during playback. The system analyzes this feedback to refine and maintain an adaptive database, ensuring that future audio outputs align with user preferences. This approach allows the system to learn and adapt over time, improving personalization and reducing the need for manual adjustments. The database may also be used to generalize preferences across similar users, enhancing efficiency and accuracy in delivering tailored audio experiences.
4. The method of claim 1 , further comprising predicting the audio level modulation to be used in an up-corning conversation between a first entity and a second or additional entity based on the historical call or conference conversation.
This invention relates to audio level modulation in communication systems, specifically for adjusting audio levels during conversations or conferences based on historical data. The problem addressed is the need for dynamic audio level adjustments to improve clarity and user experience in real-time communication, particularly in multi-party calls or conferences where audio levels may fluctuate unpredictably. The method involves analyzing historical call or conference data to predict the audio level modulation required for an upcoming conversation between at least two entities. Historical data may include past audio levels, speaker activity patterns, background noise levels, and other relevant factors. By leveraging this data, the system can preemptively adjust audio levels to optimize listening conditions, reducing the need for manual adjustments or disruptions during the conversation. The method may also include real-time monitoring of the current conversation to refine predictions and make further adjustments as needed. This ensures that the audio level modulation remains effective throughout the call or conference. The system may use machine learning or statistical models to analyze historical data and generate accurate predictions, improving over time with more data. The invention is particularly useful in scenarios where participants have varying speaking volumes, background noise, or language barriers, as it helps maintain consistent audio quality. By predicting and adjusting audio levels in advance, the system enhances communication clarity and reduces listener fatigue.
5. The method of claim 1 , further comprising adjusting the audio level modulation with cognitive feedback and indicating the audio level modulation using a visual indicator on the device.
This invention relates to audio level modulation systems that incorporate cognitive feedback and visual indicators to enhance user experience. The technology addresses the problem of ineffective audio level adjustments in devices, where users struggle to achieve optimal sound levels due to lack of real-time feedback or intuitive control mechanisms. The system dynamically adjusts audio levels based on cognitive feedback, which may include user preferences, environmental conditions, or physiological responses, ensuring personalized and adaptive sound output. Additionally, the device provides a visual indicator to clearly display the current audio level modulation, allowing users to monitor and adjust settings more effectively. This combination of cognitive feedback and visual indicators improves user interaction with audio devices, making adjustments more intuitive and responsive to individual needs. The system may be applied in various audio devices, including headphones, speakers, and smart devices, to enhance audio performance and user satisfaction.
6. The method of claim 1 , further comprising estimating an importance of the ongoing conversation based on an analysis of content of the ongoing conversation.
This invention relates to conversational analysis systems that evaluate the importance of ongoing conversations. The technology addresses the challenge of dynamically assessing the relevance or significance of real-time interactions, such as customer service calls, meetings, or other dialogue-based exchanges, to prioritize responses, allocate resources, or trigger automated actions. The method involves analyzing the content of an ongoing conversation to determine its importance. This analysis may include natural language processing (NLP) techniques to extract keywords, sentiment, or contextual cues that indicate urgency, complexity, or strategic value. For example, the system may detect keywords like "urgent," "complaint," or "contract" to infer higher importance. The analysis may also consider conversation length, speaker tone, or deviations from expected dialogue patterns. The importance estimation can be used to route conversations to appropriate personnel, escalate high-priority interactions, or trigger automated responses. For instance, a customer service system might prioritize calls flagged as high-importance for immediate agent attention. The method may also integrate with other systems, such as CRM or analytics platforms, to enhance decision-making. The invention builds on foundational techniques for real-time conversation monitoring and NLP-based content analysis, offering a way to dynamically assess and act on conversation significance. This approach improves efficiency in environments where timely and accurate prioritization of interactions is critical.
7. The method of claim 5 , further comprising adjusting the audio level with the cognitive feedback.
This invention relates to audio processing systems that use cognitive feedback to dynamically adjust audio levels. The problem addressed is the need for audio systems to adapt to user preferences or environmental conditions without manual intervention, improving user experience and accessibility. The method involves monitoring cognitive feedback, such as brainwave patterns or user responses, to assess how audio levels affect the listener. This feedback is processed to determine whether the audio level should be increased or decreased. The system then automatically adjusts the audio output based on this analysis, ensuring optimal listening conditions. The cognitive feedback may be derived from wearable sensors, EEG devices, or other biometric inputs that detect user engagement, fatigue, or discomfort. The method also includes preprocessing the audio signal to enhance clarity or reduce noise before applying the cognitive feedback-based adjustments. This ensures that the audio remains intelligible and comfortable for the listener. The system may further incorporate machine learning to refine adjustments over time, adapting to individual user preferences or changing environments. By integrating cognitive feedback into audio level control, the invention provides a more personalized and responsive audio experience, particularly useful in applications like hearing aids, virtual reality, or assistive listening devices. The system dynamically balances audio quality and user comfort, reducing the need for manual adjustments.
8. The method of claim 3 , further comprising adjusting a speech speed of the ongoing conversation based on the cognitive feedback.
This invention relates to real-time speech processing systems that adapt to user cognitive feedback during conversations. The problem addressed is the lack of dynamic adjustment in speech speed to accommodate varying user comprehension levels, which can lead to frustration or disengagement. The system monitors cognitive feedback, such as attention levels, comprehension signals, or physiological responses, to assess whether the conversation pace is optimal. Based on this feedback, the system automatically adjusts the speech speed of the ongoing conversation in real time. For example, if the feedback indicates confusion or distraction, the system may slow down the speech rate, while if the feedback suggests high engagement, it may increase the speed to maintain interest. The cognitive feedback may be derived from eye-tracking, brainwave analysis, or other biometric sensors. The system integrates with speech synthesis or real-time speech modification tools to dynamically alter the speech rate without disrupting the natural flow of the conversation. This adaptive approach ensures that the conversation remains accessible and engaging for the user, improving overall communication effectiveness.
9. The method of claim 1 , wherein the characteristic of at least one of volume and speech speed from the historical call or conference conversation of other users are summarized at the user and group level.
This invention relates to analyzing and summarizing communication patterns from historical calls or conference conversations to improve user interactions. The system captures and processes audio data from past communications, extracting key characteristics such as volume and speech speed. These characteristics are then aggregated and summarized at both the individual user level and the group level, providing insights into communication behaviors. The summarized data can be used to identify trends, optimize future interactions, or tailor communication strategies based on historical patterns. The method involves collecting audio recordings, extracting relevant features, and generating statistical summaries that reflect how users or groups typically communicate. By analyzing these patterns, the system helps users or organizations adapt their communication approaches to enhance clarity, efficiency, or engagement in future conversations. The invention is particularly useful in professional or collaborative environments where understanding communication dynamics can improve productivity and collaboration.
10. The method of claim 1 , wherein the user interaction pattern is inferred based on the user interactions with the one or more conversation devices.
A system and method for analyzing user interactions with conversation devices to infer user interaction patterns. The technology domain involves natural language processing, user behavior analysis, and conversational interfaces. The problem being solved is the need to accurately predict or infer user interaction patterns based on historical or real-time interactions with conversation devices, such as virtual assistants, chatbots, or voice-enabled systems. This helps improve user experience, personalize responses, and optimize system performance. The method involves collecting data from user interactions with one or more conversation devices, where these interactions may include voice commands, text inputs, or other forms of communication. The system processes this interaction data to identify patterns, such as frequency of use, preferred input methods, common queries, or contextual cues. Machine learning or statistical models may be applied to analyze the data and infer likely future interactions or user preferences. The inferred patterns can then be used to adapt the conversation device's behavior, such as suggesting responses, preemptively providing information, or adjusting interaction styles. The system may also account for environmental factors, such as time of day, location, or device context, to refine pattern inference. By continuously learning from user interactions, the system improves its ability to anticipate user needs and enhance engagement. This approach is particularly useful in smart home systems, customer service bots, or any application requiring dynamic adaptation to user behavior.
11. A computer program product for volume and speech frequency level adjustment, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform: learning a preferred level and a characteristic of at least one of volume and speech frequency from a historical call or conference conversation; detecting a context characteristic of an ongoing conversation and an interaction of a user with a device; determining a cognitive state and a contextual situation of the user in relation to the ongoing conversation as a function of at least one of the context characteristic, a preferred level and a characteristic of the volume or the speech frequency, and the interaction; determining at least one factor to trigger an audio level modulation based on the function; and dynamically adjusting audio levels of the ongoing conversation for the user based on the at least one factor.
This invention relates to audio adjustment systems for improving communication clarity in calls or conferences. The problem addressed is the static nature of traditional volume and frequency settings, which fail to adapt to real-time user needs, cognitive states, or contextual situations. The solution involves a computer program that dynamically adjusts audio levels based on learned preferences, contextual analysis, and user interaction patterns. The system first learns a user's preferred volume and speech frequency levels from historical call or conference data, capturing individual preferences and communication habits. During an ongoing conversation, it detects contextual characteristics, such as background noise or conversation topic, and monitors user interactions with their device, such as touch or voice commands. Using this data, the system determines the user's cognitive state (e.g., focus level) and contextual situation (e.g., multitasking). It then identifies factors that may require audio modulation, such as sudden noise or user distraction. Finally, the system dynamically adjusts the audio levels of the ongoing conversation in real-time to optimize clarity and comfort for the user. The adjustments may include volume changes or frequency modifications to enhance speech intelligibility. This approach ensures adaptive audio optimization without manual intervention.
12. The computer program product of claim 11 , further comprising receiving, analyzing and storing a plurality of preferred levels and characteristics of the volume adjustment data sources including: a historical volume of the audio level of a given user; a historical call or conference conversation; a user feedback; a user-profile; and data from one or more sensors of a call or conversation device including historical interactions of a user with the conversation device.
This invention relates to audio volume adjustment in communication systems, specifically optimizing volume levels based on user preferences and contextual data. The system dynamically adjusts audio levels during calls or conferences by analyzing multiple data sources to enhance user experience and reduce manual adjustments. Key data sources include historical audio volume levels of a specific user, past call or conference interactions, user feedback, user profiles, and sensor data from communication devices. Sensors may track user interactions with the device, such as touch inputs or proximity, to infer preferred volume settings. The system processes this data to determine optimal volume adjustments, ensuring consistency across different calls and devices. By leveraging historical patterns and real-time feedback, the system adapts to individual preferences, improving comfort and reducing disruptions. The invention aims to automate volume management, eliminating the need for manual adjustments and enhancing communication clarity. The solution is particularly useful in environments where audio conditions vary, such as noisy settings or multi-device scenarios.
13. The computer program product of claim 11 , further comprising creating and progressively maintaining a database of the preferred level and the characteristic of the volume or the speech speed with cognitive feedback for a plurality of users.
This invention relates to speech processing systems that adapt to user preferences and cognitive feedback. The problem addressed is the lack of personalized speech output in digital systems, which can lead to user frustration or inefficiency. The invention involves a computer program that analyzes user interactions with speech output, such as volume levels or speech speed, and adjusts these parameters based on user preferences and cognitive feedback. The system creates and maintains a database that stores these preferences and characteristics for multiple users, allowing the system to progressively refine its adjustments over time. The database tracks how users respond to different speech settings, enabling the system to optimize speech output for each individual. This adaptive approach ensures that speech output remains comfortable and efficient for users, improving overall usability. The system may also incorporate feedback mechanisms, such as explicit user input or implicit behavioral cues, to further refine the speech settings. By maintaining a centralized database, the system can apply learned preferences across different devices or sessions, providing a consistent and personalized experience. This invention enhances accessibility and user satisfaction in speech-based applications.
14. The computer program product of claim 11 , further comprising predicting the audio level modulation to be used in an up-coming conversation between a first entity and a second entity or entities based on the historical call or conference conversation.
This invention relates to audio level modulation in communication systems, specifically predicting and adjusting audio levels for upcoming conversations based on historical call or conference data. The technology addresses the problem of inconsistent audio volume during interactions, which can lead to poor user experience, miscommunication, or fatigue. By analyzing past conversations, the system identifies patterns in audio levels, speaker activity, and environmental noise to anticipate and optimize audio modulation for future interactions. The system collects and processes historical call or conference data, including audio level variations, speaker participation, and background noise levels. Machine learning or statistical models are trained on this data to predict optimal audio modulation settings for upcoming conversations between the same or similar participants. The prediction accounts for factors such as speaker dominance, noise conditions, and historical volume adjustments. The predicted modulation settings are then applied automatically during the next conversation to ensure consistent and comfortable audio levels for all participants. This approach improves communication clarity and reduces manual adjustments, enhancing user experience in voice calls, video conferences, and other real-time audio interactions. The system may also adapt in real-time if deviations from predicted patterns occur.
15. The computer program product of claim 11 , further comprising adjusting the audio level modulation with cognitive feedback and indicating the audio level modulation using a visual indicator on the device.
This invention relates to audio level modulation in electronic devices, particularly for improving user experience by dynamically adjusting audio levels based on cognitive feedback. The problem addressed is the lack of adaptive audio control that responds to user engagement or cognitive load, which can lead to suboptimal listening experiences. The invention provides a computer program product that includes instructions for modulating audio levels in response to detected cognitive feedback, such as user attention or mental workload. The system may use sensors or user input to assess cognitive state and adjust audio levels accordingly. Additionally, the invention includes a visual indicator on the device to inform the user of the current audio level modulation state, enhancing transparency and user control. The visual indicator may display the intensity or type of modulation being applied, allowing users to understand and interact with the system more effectively. This approach ensures that audio output is tailored to the user's cognitive context, improving comfort and engagement. The invention may be implemented in devices such as smartphones, headphones, or other audio-enabled systems where adaptive audio control is beneficial.
16. The computer program product of claim 11 , further comprising estimating an importance of the ongoing conversation based on an analysis of content of the ongoing conversation.
This invention relates to a computer program product for analyzing and managing ongoing conversations, particularly in digital communication systems. The technology addresses the challenge of dynamically assessing the relevance or significance of conversations in real-time, which is crucial for applications like customer support, virtual assistants, or automated moderation. The system includes a method for processing an ongoing conversation by extracting features from the conversation content, such as keywords, sentiment, or contextual cues. These features are then analyzed to determine the conversation's importance, which could be based on factors like urgency, topic relevance, or user engagement. The analysis may involve natural language processing (NLP) techniques, machine learning models, or rule-based systems to evaluate the conversation's significance. The importance estimation can be used to prioritize responses, trigger automated actions, or route the conversation to appropriate personnel. For example, a highly important conversation might bypass standard queues or receive immediate attention. The system may also adapt its behavior based on the estimated importance, such as adjusting response templates or escalation protocols. Additionally, the system may incorporate historical data or user profiles to refine the importance assessment. This ensures that the evaluation aligns with contextual or domain-specific criteria, improving accuracy over time. The overall goal is to enhance efficiency and user satisfaction by dynamically adapting to the evolving needs of the conversation.
17. A volume and speech frequency level adjustment system, the system comprising: a processor; and a memory, the memory storing instructions to cause the processor to perform: learning a preferred level and a characteristic of at least one of volume and speech frequency from a historical call or conference conversation; detecting a context characteristic of an ongoing conversation and an interaction of a user with a device; determining a cognitive state and a contextual situation of the user in relation to the ongoing conversation as a function of at least one of the context characteristic, a preferred level and a characteristic of the volume or the speech frequency, and a user interaction pattern with one or more call or conversation devices; determining at least one factor to trigger an audio level modulation based on the function; and dynamically adjusting audio levels of the ongoing conversation for the user based on the at least one factor.
This system relates to audio adjustment in communication devices, addressing the problem of static volume and speech frequency settings that fail to adapt to varying user needs and environmental conditions. The system dynamically adjusts audio levels during calls or conferences by analyzing historical usage patterns, current context, and user interactions to optimize listening comfort and clarity. The system includes a processor and memory storing instructions to perform several functions. First, it learns a user's preferred volume and speech frequency levels from past calls or conversations, storing these preferences as a baseline. During an ongoing conversation, it detects context characteristics, such as background noise or device usage patterns, and monitors the user's interaction with the device, such as touch or voice commands. Using this data, the system determines the user's cognitive state and situational context, such as whether they are distracted or focused. Based on these factors, the system identifies triggers for audio adjustments, such as sudden noise spikes or user fatigue indicators. It then dynamically modifies the audio levels of the ongoing conversation in real-time, adjusting volume or speech frequency to enhance intelligibility and reduce strain. The adjustments are personalized, ensuring the audio output aligns with the user's preferences and current environment. This approach improves communication quality by adapting to real-time conditions rather than relying on fixed settings.
18. The system of claim 17 , further comprising receiving, analyzing and storing a plurality of preferred levels and characteristics of the volume adjustment data sources including: a historical volume of audio level of a given user; a historical call or conference conversation; a user feedback; a user profile; and data from one or more sensors of a conversation device including historical interactions of a user with the conversation device.
This invention relates to a system for dynamically adjusting audio volume levels in communication devices, such as during calls or conferences, to enhance user experience. The problem addressed is the lack of personalized and context-aware volume adjustments, which can lead to discomfort or poor audio quality. The system analyzes and stores multiple data sources to determine optimal volume settings. These sources include historical audio levels of a specific user, past call or conference conversations, user feedback, user profiles, and sensor data from the communication device. The sensor data may track user interactions with the device, such as adjustments made during previous sessions. By leveraging this data, the system can predict and apply preferred volume levels automatically, adapting to individual preferences and environmental conditions. The goal is to provide seamless, personalized audio adjustments without manual intervention, improving clarity and comfort during communication. The system may also integrate real-time feedback to refine volume settings further, ensuring continuous optimization. This approach enhances user satisfaction by tailoring audio output to individual needs and usage patterns.
19. The computer program product of claim 17 , further comprising creating and progressively maintaining a database of the preferred level and the characteristic of the volume or the speech frequency with cognitive feedback for a plurality of users.
This invention relates to a system for optimizing audio output based on user preferences and cognitive feedback. The system addresses the problem of generic audio settings that do not adapt to individual user needs, leading to suboptimal listening experiences. The invention involves analyzing user interactions with audio content to determine preferred volume levels and speech frequency characteristics. These preferences are stored in a database, which is progressively updated as users continue to interact with the system. Cognitive feedback, such as user adjustments to volume or frequency settings, is used to refine the database entries over time. The system may also include a method for dynamically adjusting audio output in real-time based on the stored preferences and feedback. This ensures that audio content is tailored to each user's preferences, improving clarity and comfort. The database can be shared across multiple users, allowing the system to learn from collective feedback and improve overall performance. The invention aims to enhance audio personalization by continuously adapting to user behavior and feedback.
20. The system of claim 17 , wherein the user interaction pattern is inferred based on the user interactions with the one or more conversation devices.
A system for analyzing user interactions with conversation devices, such as virtual assistants or chatbots, to infer user interaction patterns. The system monitors and records user inputs, responses, and behavioral data during interactions with these devices. By processing this data, the system identifies recurring patterns in how users engage with the conversation devices, such as preferred input methods, common queries, or interaction frequency. These inferred patterns are then used to optimize the performance of the conversation devices, personalize responses, or improve user experience. The system may also adapt its behavior based on detected patterns, such as suggesting relevant follow-up questions or adjusting response styles. The underlying technology involves natural language processing, machine learning, and data analytics to extract meaningful insights from user interactions. This approach enhances the efficiency and effectiveness of conversation devices by tailoring their responses to individual user behaviors and preferences. The system may be integrated into various applications, including customer service platforms, virtual assistants, or automated support systems, to provide more intuitive and responsive interactions.
Unknown
May 12, 2020
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